Search Results for author:
Found 15 papers, 8 papers with code
Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning
Evolutionary computation (EC) algorithms, renowned as powerful black-box optimizers, leverage a group of individuals to cooperatively search for the optimum.
Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution
As a proof-of-principle study, we apply this framework to a group of Differential Evolution algorithms.
LLaMoCo: Instruction Tuning of Large Language Models for Optimization Code Generation
Recent research explores optimization using large language models (LLMs) by either iteratively seeking next-step solutions from LLMs or directly prompting LLMs for an optimizer.
Large Language Model with Graph Convolution for Recommendation
To adapt text-based LLMs with structured graphs, We use the LLM as an aggregator in graph processing, allowing it to understand graph-based information step by step.
Symbol: Generating Flexible Black-Box Optimizers through Symbolic Equation Learning
Recent Meta-learning for Black-Box Optimization (MetaBBO) methods harness neural networks to meta-learn configurations of traditional black-box optimizers.
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set.
MetaBox: A Benchmark Platform for Meta-Black-Box Optimization with Reinforcement Learning
To fill this gap, we introduce MetaBox, the first benchmark platform expressly tailored for developing and evaluating MetaBBO-RL methods.
Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles
Current testing scenarios typically employ predefined or scripted BVs, which inadequately reflect the complexity of human-like social behaviors in real-world driving scenarios, and also lack a systematic metric for evaluating the comprehensive intelligence of AVs.
FedHQL: Federated Heterogeneous Q-Learning
Federated Reinforcement Learning (FedRL) encourages distributed agents to learn collectively from each other's experience to improve their performance without exchanging their raw trajectories.
Decision-making with Speculative Opponent Models
To address this issue, we introduce Distributional Opponent-aided Multi-agent Actor-Critic (DOMAC), the first speculative opponent modelling algorithm that relies solely on local information (i. e., the controlled agent's observations, actions, and rewards).
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i. e., uniform).
Efficient Neural Neighborhood Search for Pickup and Delivery Problems
We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs).
Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee
The growing literature of Federated Learning (FL) has recently inspired Federated Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better decision-making policy without sharing raw trajectories.
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i. e., cyclic sequences).
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing Problem
To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step.
